Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add buddyh/agent-skills --skill deep-divegit clone --depth 1 https://github.com/buddyh/agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/buddyh/agent-skills/deep-dive)<a href="https://agentmods.dev/skills/buddyh/agent-skills/deep-dive"><img src="https://agentmods.dev/badge/skills/buddyh/agent-skills/deep-dive.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00081 | $0.03175 |
| Opus 5 | $0.00041 | $0.01588 |
| Sonnet 5 | $0.00016 | $0.00635 |
| Haiku 4.5 | $0.00008 | $0.00317 |
Grade A, and why
deep-dive scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 341 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Dive
Produce comprehensive research reports by decomposing topics into subtopics, researching each in parallel with exhaustive source gathering, and synthesizing into a single deeply-analyzed report.
Trigger
/deep-dive <topic> or when user asks for a deep dive, deep research, or comprehensive analysis of a topic.
Phase 1: Scope and Decompose (do this yourself, no agent)
1a: Assess Topic and Set Research Tier
Before decomposing, assess the topic to determine how much research it warrants:
| Focused | Standard | Comprehensive | |
|---|---|---|---|
| When | Narrow comparison, specific technical question, well-defined scope | Most topics -- technology analysis, trend report, landscape overview | Broad survey, emerging field, multi-stakeholder analysis |
| Subtopics | 5-7 | 7-9 | 9-12 |
| Word target | 4,000-6,000 | 6,000-8,000 | 8,000-10,000+ |
| Source target | 40-60 | 60-80 | 80-100+ |
| Example | "WebTransport vs WebSockets" | "State of real-time web protocols in 2026" | "Complete guide to building real-time infrastructure" |
Choose the tier based on:
- Breadth: How many distinct dimensions does the topic have?
- Maturity: Established topics have more sources; emerging ones may max out at fewer
- Comparison surface: Binary comparisons need fewer subtopics than landscape surveys
- User signal: If user says "quick" or "focused", go Focused. If "comprehensive" or "exhaustive", go Comprehensive. Default to Standard.
1b: Interpret the Research Question
Determine:
- Scope: Is this a comparison, a landscape survey, a how-to, a trend analysis, or an investigation?
- Audience: Technical practitioners, executives, researchers, or general?
- Depth signals: What would a domain expert want to know that a surface-level article would miss?
1c: Generate Subtopics
Generate subtopics according to the selected tier. Each subtopic should be:
- Distinct (minimal overlap)
- Specific enough to yield targeted search results
- Substantial enough to produce 500-1500 words of findings (scale with tier)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 341 lines · 81 tokens per session scan A f443bbb27651
deep-dive is a skill published in the GitHub repository buddyh/agent-skills (5 stars, last pushed 1mo ago), licensed MIT. It adds 81 tokens to every session and 3,175 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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